Recurrent neural network (RNN) [1]} derived from the feedforward neural networks can use their interval states (memory) to process variable length sequences of data suitable for the sequential data. Long Short-Term Memory (LSTM) has been introduced by Hochreiter and Schmidhuber [2]} which overcomes the vanishing and exploding gradient problem in RNN and have long dependencies that proved to be very promising for modelling of sequential data. A common LSTM unit is composed of a cell, an input gate, an output gate and a forget gate. The cell remembers values over arbitrary time intervals and the three gates regulate the flow of information into and out of the cell. For a given input sequence \( x=(x_{1}, x_{2},\ldots x_{T}) \)  from time \( t=1 \)  to \( T \) , LSTM calculates an output sequence \( y=y_{1},y_{2},\ldots y_{T} \) , mathematically represented as [2]}:
\(i_{i}=\sigma (\mathbf {W}_{ix}x_{t}+\mathbf {W}_{im}m_{t-1}+\mathbf {W}_{ic}c_{t-1}+b_{i})\) 
\(i_{f}=\sigma (\mathbf {W}_{fx}x_{t}+\mathbf {W}_{fm}m_{t-1}+\mathbf {W}_{fc}c_{t-1}+b_{f})\) 
\(c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot g(\mathbf {W}_{cx}x_{t}+\mathbf {W}_{cm}m_{t-1}+b_{c})\) 
\(o_{t}=\sigma (\textbf {W}_{ox}x_{t}+\mathbf {W}_{om}m_{t-1}+\mathbf {W}_{oc}c_{t-1}+b_{o})\) 
\(m_{t}=o_{t}\odot h(c_{t})\) 
\(y_{t}=\phi (\mathbf {W}_{ym}m_{t}+b_{y})\) 
